Pattern recognition is a branch of artificial intelligence concerned with the systematic classification or description of observations. Pattern recognition aims to classify visual data (in particular, patterns) based either on a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified are generally groups of measurements or observations, defining points in an appropriate multidimensional space.
Techniques in Computer-Aided Detection (CAD) mammography, a promising tool in diagnostic breast imaging, apply pattern recognition algorithms to digital mammographic images. Using various pattern recognition utilities, the mammography CAD system helps the radiologist to identify abnormalities that might otherwise have been overlooked in the breast image.
A sizeable percentage of the abnormalities (particularly true-positive or TP indications) detected in the mammogram are microcalcifications (MCCs). Microcalcifications are tiny deposits of calcium that can indicate likely breast cancer sites, particularly when they appear to be grouped as microcalcification clusters. An MCC cluster itself comprises a plurality of MCC spots, each of which, in turn, comprises a plurality of mammographic image pixels.
When a mammography CAD system uses pattern recognition algorithms to detect lesions in mammographic images, some error is inevitable. As a result, normal structures that resemble lesion patterns may be inaccurately classified as abnormalities. These mis-classified normal structures are called false positives (FPs).
An efficient CAD algorithm yields a high true-positives (TPs) rate while keeping the number of false-positives (FPs) to a minimum. In studying the performance of existing CAD utilities using digital or film-based mammograms, it has been found that many FP MCC candidates identified by mammography CAD systems lie on or near normal features that are generally linear such as blood vessels. Removing those MCC candidates that are associated with linear structures but do not represent likely true-positives can significantly reduce the overall FP rate. However, in practice, there seem to be unlimited variations in the appearance of linear structures in terms of contrast, brightness, texture and morphological shapes, and other characteristics.
Various methods for extracting linear structures have been proposed, with significant differences between the different approaches. One promising approach has been implemented as a multi-scale line operator, for example, with intuitively convincing results. The output of such a method can then be used for classifying linear structures.
The overall function of such a line operator can be described as follows: The line operator takes the average grey level of the pixels lying on an oriented local line passing through the target pixel, and subtracts the average intensity of all the pixels in the locally oriented neighborhood. The line strength is compared for n orientations. Line direction is obtained from the orientation that provides maximum line strength. Scale information can be obtained by applying the line operator to images that are rescaled by Gaussian smoothing and sub-sampling. For each pixel, the scale that produces the maximum line strength is taken as the detected line scale.
Another method estimates the intensity profile of curvilinear structures (CLS) in mammograms in a single scale. In this type of method, the CLS are assumed to have circular cross section when the breast is not compressed. The cross section of CLS in the mammogram is assumed to be elliptical. Candidate pixels for CLS are detected using the response of a second order difference operation which is applied in four directions. If there is a sufficient high response for one of the orientations, the pixel forms part of a CLS. A measure of line strength is obtained by determining the contrast of the line profile at these pixels. Other researchers have adopted this two step method and devised a multi-resolution ridge detector for structures ranging from 1800 microns to 180 microns, for example. Additional improvements to this method enhance the performance of the detector by using local energy thresholding to suppress undesirable response from noise. The local energy is also used to determine CLS junctions.
Alexander Schneider et al. (U.S. Patent Application Publication No. US 2002/0159622 proposes a system and method for detecting lines in medical images. The method describes a direction image array and a line image array are formed by filtering a digital image with a single-peaked filter, convolving the regular array with second-order difference operators oriented along the horizontal, vertical, and diagonal axes, and computing the direction image arrays and line image arrays as direct scalar functions of the results of the second order difference operations. Schneider et al. noted that, if the four line operator function correspond to the special orientations of 0, 45, 90 and 135 degrees, line detection based on the use of four line operator functions requires fewer computations than line detection based on the use of three line operator functions.
FP reduction, although addressed using a number of different approaches, remains a problem. One type of approach for FP identification uses features extracted from spatial and morphology domains, including gray-level description, shape description and clusters description. However, researchers have not directed their attention to the use of features directly related to linear structures for this purpose.
It has been held by some researchers that the results from a multi-resolution ridge detector could be beneficial to false-positive MCC reduction, but there has been no conclusive evidence of such a reduction. Moreover, it can be computationally inefficient to generate actual linear structures merely for the purpose of confirming the association of an MCC candidate cluster with a linear structure in mammography CAD. (Note that an MCC candidate cluster is a cluster that is under testing for cancerous lesions.)
Thus, although researchers have explored the relationship of MCC clusters to nearby linear features in various ways and have attempted to classify various groupings of MCC spots in order to detect likely cancer sites, results achieved thus far indicate that there is still considerable room for improvement. It is desirable to be able to identify TPs wherever possible in the mammography image. At the same time, any increase in the relative number of FPs can make a mammography system difficult to use effectively as a diagnostic tool.
No matter how well CAD algorithms can be made to perform, it is observed that CAD processing is primarily a tool for assisting the diagnostician and, as such, has its limitations. Ultimately, diagnosis itself relies on the judgment of the medical practitioner, who may be guided and heavily influenced by mammography CAD results. One problem in assessing CAD output relates to inherent difficulties in visual perception when the mammographic image is displayed, particularly where MCC clusters may be detectable using a proven algorithm but are difficult to discern clearly and appear against a relatively “noisy” background. Existing systems are not adaptable or configurable to compensate for such conditions, but, instead, force the viewer to overcome difficulties in visually identifying and classifying potential MCC features. This difficulty, in turn, can tend to reduce the value of improved CAD techniques, since their results may not provide clear diagnostic information to the viewing practitioner when the image is displayed.
Therefore, not only is an improved approach for microcalcification detection in mammography CAD of value; it is also desirable that the image content display apparatus and display methods be used more effectively to aid the diagnostician in utilizing mammography CAD detection results for making diagnostic decisions.